Federico Sau
Technical University of Madrid
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Featured researches published by Federico Sau.
Global Change Biology | 2014
Simona Bassu; Nadine Brisson; Jean Louis Durand; Kenneth J. Boote; Jon I. Lizaso; James W. Jones; Cynthia Rosenzweig; Alex C. Ruane; Myriam Adam; Christian Baron; Bruno Basso; Christian Biernath; Hendrik Boogaard; Sjaak Conijn; Marc Corbeels; Delphine Deryng; Giacomo De Sanctis; Sebastian Gayler; Patricio Grassini; Jerry L. Hatfield; Steven Hoek; Cesar Izaurralde; Raymond Jongschaap; Armen R. Kemanian; K. Christian Kersebaum; Soo-Hyung Kim; Naresh S. Kumar; David Makowski; Christoph Müller; Claas Nendel
Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.
Agricultural Systems | 2001
B. Ruiz-Nogueira; Kenneth J. Boote; Federico Sau
Abstract Crops such as soybean ( Glycine max L.) are grown predominantly under rainfed conditions where water is a major limiting factor and the interannual variability in rainfall pattern is high. Crop modeling has proven a valuable tool to evaluate the long-term consequences of weather patterns, but the candidate crop models must be tested and calibrated for new regions prior to their use as extrapolation tools to predict optimum cultivar choice and sowing dates. The objectives of this paper were to calibrate the CROPGRO-soybean model for growth and yield under rainfed conditions in Galicia, northwest Spain, and then to use the calibrated model to establish the best sowing dates for three cultivars at three locations in this region. The starting point of the calibration process was the CROPGRO-soybean version previously calibrated for non-limiting water conditions. The original model, when simulated versus rainfed soybean field data sets, tended to simulate more severe water stress than actually occurred. In order to calibrate growth and yield for the actual soil we tried several ways for the modelled crop to have access to more water. Modifications were made on soil depth, water holding capacity, and root elongation rate. In addition, other changes were made to predict accurately the observed water-stress induced acceleration of maturity. Long-term simulations with recorded weather data showed that soybean is more sensitive to planting date under irrigated than rainfed management, in the three studied Galician locations.
Field Crops Research | 1999
Federico Sau; Kenneth J. Boote; B. Ruiz-Nogueira
Abstract This paper describes the evaluation and improvement of the CROPGRO-soybean model for a cool environment in northwest Spain. The model was evaluated with a soybean (Glycine max L.) 1994 field data set with three cultivars and three planting dates. The original model proved to underestimate biomass and seed yield, so modifications were made to the temperature functions affecting N2 fixation and photosynthesis, in order to fit better to the experimental data. The modified model was then tested, i.e., validated, with independent experimental data collected in 1995 at the same site with the same cultivars and four planting dates. Comparing observed with simulated data in 1994, the modified model decreased the root mean square error (RMSE) for biomass at harvest and seed yield from 1714 to 466 and from 940 to 333, respectively. For 1995, the validation year, RMSE for biomass decreased from 1366 to 352, although the yield was now overestimated with no significant change in RMSE. The average simulated harvest index (HI) for 1995 was greater than for 1994, the reverse of the measured values. Nevertheless, the modified model was more reliable in predicting crop performances under these cool conditions than the original model. Moreover, the predictions of the modified model for a warm climate (Gainesville, FL) were quite acceptable. We conclude that the modified model can be used successfully over a wider range of climates than the original version.
Agronomy Journal | 2008
Francisco Xavier López-Cedrón; Kenneth J. Boote; Juan Piñeiro; Federico Sau
Field Crops Research | 2010
Adriana Confalone; Jon I. Lizaso; B. Ruiz-Nogueira; Francisco-Xavier López-Cedrón; Federico Sau
Hortscience | 2001
Santiago Pereira-Lorenzo; Ana María Ramos-Cabrer; Belén Díaz-Hernández; Javier Ascasíbar-Errasti; Federico Sau; Marta Ciordia-Ara
Response of Crops to Limited Water: Understanding and Modeling Water Stress Effects on Plant Growth Processes | 2008
K. J. Boote; Federico Sau; Gerrit Hoogenboom; James W. Jones
Scientia Horticulturae | 2012
Santiago Pereira-Lorenzo; Allívia Rouse Ferreira dos Santos; Ana María Ramos-Cabrer; Federico Sau; María Belén Díaz-Hernández
Pastos: Revista de la Sociedad Española para el Estudio de los Pastos, ISSN 0210-1270, 2006-12, No. 36 | 2006
F. X. Cedrón; B. Ruiz-Nogueira; Adriana Confalone; J. Piñeiro; Federico Sau
Agronomy Journal | 2011
Adriana Confalone; Kenneth J. Boote; Jon I. Lizaso; Federico Sau